lintsampler: a new way to quickly get random samples from any distribution
lintsampler is a pure Python package that can easily and efficiently generate random samples from any probability distribution.- 26176Murphy2025-03-22
What Goes Into AI? Exploring the GenAI Technology Stack
You've heard of OpenAI and Nvidia, but do you know who else is involved in the AI wave and how they all fit together?- 27652Murphy2025-03-22
Product-Oriented ML: A Guide for Data Scientists
How to build ML products users love- 20360Murphy2025-03-22
Cruel Summer
Lessons Learned from the Worst Tech Job Market in 20 Years- 22135Murphy2025-03-22
Linear Discriminant Analysis (LDA)
Discover how LDA helps identify critical data features- 23520Murphy2025-03-22
How to Set Bid Guardrails in PPC Marketing
Without controls, bidding algorithms can be quite volatile. Learn how to protect performance through adding guardrails.- 24620Murphy2025-03-22
Top 5 Principles for Building User-Friendly Data Tables
Designing intuitive and reliable tables that your data team will love- 21489Murphy2025-03-22
Tracking Hurricanes With AI Weather Models
Storm chasing for data scientists: A Hurricane Milton case study- 29029Murphy2025-03-22
Automatic Differentiation (AutoDiff): A Brief Intro with Examples
An introduction to the mechanics of AutoDiff, exploring its mathematical principles, implementation strategies, and applications- 20976Murphy2025-03-22
The Two Sides of Hiring: Recruiting vs. Interviewing for Data Roles in Diverse Markets
Factors of success in recruiting and interviewing after applying for 150+ positions and reviewing 500+ CVs in 4 different countries- 26629Murphy2025-03-22
PyTorch Optimizers Aren't Fast Enough. Try These Instead
These 4 advanced optimizers will open your mind.- 20792Murphy2025-03-22
Nine Rules for Running Rust on Embedded Systems
Practical Lessons from Porting range-set-blaze to no_std- 26568Murphy2025-03-22
Topic Alignment for NLP Recommender Systems
Leveraging topic modeling to align user queries with document themes, enhancing the relevance and contextual accuracy of recommendations- 21390Murphy2025-03-22
Florence-2: Advancing Multiple Vision Tasks with a Single VLM Model
A Guided Exploration of Florence-2's Zero-Shot Capabilities: Captioning, Object Detection, Segmentation and OCR.- 20487Murphy2025-03-22
How to Perform A/B Testing with Hypothesis Testing in Python: A Comprehensive Guide
A Step-by-Step Guide to Making Data-Driven Decisions with Practical Python Examples- 23388Murphy2025-03-22
A Mixed-Methods Approach to Offline Evaluation of News Recommender Systems
Combining reader feedback from surveys with behavioral click data to optimize content personalization.- 27617Murphy2025-03-22
Understanding Automatic Differentiation in JAX: A Deep Dive
Unleashing the Gradient: How JAX Makes Automatic Differentiation Feel Like Magic- 21268Murphy2025-03-22
Common Misconceptions About Data Science
Data science advice that you should question- 20935Murphy2025-03-22
How to Choose the Best ML Deployment Strategy: Cloud vs. Edge
The choice between cloud and edge deployment could make or break your project- 22984Murphy2025-03-22
Bursting the AI Hype Bubble Once and for All
Misinformation and poor research: a case study- 21257Murphy2025-03-22
The current state of continual learning in AI
Why is ChatGPT only trained up until 2021?Optimizing Pandas Code: The Impact of Operation Sequence
Learn how to rearrange your code to achieve significant speed improvements.